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GPU accelerate parallel Odd-Even merge sort: An OpenCL method

机译:GPU加速并行奇偶合并合并:OpenCL方法

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Odd-Even merge sort is a basic problem in computer supported cooperative work in design area. However, it is not effective because of the high complexity O(nlg2n) in CPU platform. In this paper, we present a novel implementation based on the OpenCL programming model on recent GPU (Graphic Processing Unit). Our implementation was based on Knuth''s algorithm and do some change. Due to limitations of OpenCL, we utilize a flag variable to make it avoid the direct backward control flow. As results, our implementation achieves 18× speedups compared with the CPU C++ STL quick sort. And it gets almost linear speedup for next generations of GPU because of the complete parallelism in each iteration process. Meanwhile, our approach makes the odd-even merge sort effectively in practice because of the high performance. Furthermore, the approach used in this paper for cooperating thousands of processing units to parallel process can also be used in other cooperation areas.
机译:奇偶合并排序是设计领域中计算机支持的协作工作中的一个基本问题。但是,由于CPU平台的复杂度O(nlg 2 n)高,因此无效。在本文中,我们提出了一种基于OpenCL编程模型的最新实现,该模型在最近的GPU(图形处理单元)上实现。我们的实现基于Knuth的算法并进行了一些更改。由于OpenCL的限制,我们利用标志变量来避免直接向后控制流。结果,与CPU C ++ STL快速排序相比,我们的实现实现了18倍的加速。由于每个迭代过程都具有完全的并行性,因此它对于下一代GPU几乎实现了线性加速。同时,由于其高性能,我们的方法在实践中有效地进行了奇偶合并排序。此外,本文中用于将成千上万个处理单元进行并行处理的方法也可以用于其他合作领域。

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